Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Intelligence System via Machine Learning Algorithms in Detecting the Moisture Content Removal Parameters of Seaweed Big Data

The parameters that determine the removal of moisture content have become necessary in seaweed research as they can reduce cost and improve the quality and quantity of the seaweed. During the seaweed’s drying process, many drying parameters are involved, so it is hard to find a model that can determine the drying parameters. This study compares seaweed big data performance using machine learning algorithms. To achieve the objectives, four machine learning algorithms, such as bagging, boosting, support vector machine, and random forest, were used to determine the significant parameters from the data obtained from v-GHSD (v-Groove Hybrid Solar Drier). The mean absolute percentage error (MAPE) and coefficient of determination (R2) were used to assess the model. The importance of variable selection cannot be overstated in big data due to the large number of variables and parameters that exceed the number of observations. It will reduce the complexity of the model, avoid the curse of dimensionality, reduce cost, remove irrelevant variables, and increase precision. A total of 435 drying parameters determined the moisture content removal, and each algorithm was used to select 15, 25, 35 and 45 significant parameters. The MAPE and R-Square for the 45 highest variable importance for random forest are 2.13 and 0.9732, respectively. It performed best, with the lowest error and the highest R-square. These results show that random forest is the best algorithm to decide the vital drying parameters for removing moisture content.

Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA’s GEDI Spaceborne LiDAR

Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia’s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.

Assessing scale-dependent effects on Forest biomass productivity based on machine learning

Estimating forest above-ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30-ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.